The present invention relates generally to a route-quality learning method, and more particularly, but not by way of limitation, to a system, method, and computer program product for cognitive-enabled reasoning and learning about previous actions (e.g., transport routing) leveraging the pervasive sensing of Internet of Things (IoT) in the traffic space.
Conventionally, tools for personalized traffic information on mobile devices have been developed in which traffic predictions along a driver's usual route were provided in advance of the journey. This leverages the pervasive sensing available in IoT capabilities of mobile and in-vehicle devices. However, the conventional techniques have limitations, such as, the driver's route taken may not have been the best choice, but the driver is unable to know whether or not it is the case since only an after-the-fact service could provide such information and this precludes effective learning. Further, the conventional routing applications may be unable to integrate unstructured sources of information that the driver is accumulating, such as crowd sentiment, social events along the routes, driving behaviors, route preferences, etc. thereby neglecting crucial data sources in their learning process.